5th Jul 2021
In the next instalment of our Digital Twin explainer series, Ruggiero Guida, Associate Director & Chief Product Manager here at IES, explains the importance of considering the use case of your digital twin and ensuring it is fit for purpose.
The digital twin concept has garnered serious attention in recent years and the use of such technology is predicted to continue to grow rapidly over the coming years. As a result, the market has been flooded with providers claiming to offer their own digital twin solutions to a variety of ends. But how useful are they really? And how can prospective users cut through the babble of digital twin marketing speak to make sure they get the right technology that will be of true value to them?
This is why in this explainer I wanted to focus on the Digital Twin Use Case and the importance of ensuring you have a clear plan of what you want to get out of the technology before embarking on your Digital Twin journey. At IES, we focus on providing digital twin solutions specifically for the built environment, so here I will be referring specifically to digital twins in this context and, mostly, buildings with their associated systems.
In several implementations of digital twins for the built environment there is often too much focus on 3D visualisation. A lot of effort is employed to create very realistic, very appealing, virtual models. Although the visual aspect can be important in some contexts, such architectural rendering, I believe it is mainly aesthetic, it is not fundamental and other key aspects of the digital twin are more important.
The main point of a digital twin is that it should be useful and fit for purpose, that it has a goal and that it helps its users to achieve a goal: for example, improving a product, making a prediction or simply exposing characteristics of the physical asset that are not immediately visible.
A famous quote from the British statistician George Box says: “all models are wrong, but some are useful”. This aphorism is certainly true for the digital twin as well. The only way to perfectly replicate reality would be to build a model that is so complex that it would be probably impossible to manage. The objective, when building or defining a digital twin, should be to hone in on those ‘features’ of the digital asset that are useful for the analysis at hand.
If the purpose is to build a digital twin of a portfolio of buildings and the goal is to decarbonise the portfolio, to reduce costs and the only information available is data from sensors, IoT devices, smart meters then a digital twin can be built that uses only this information and the visual component can be safely ignored.
Of course, there is always room for improvement when dealing with such a complex model; data about the environment such as weather from a nearby weather station or relevant data from other web services can always be used to enrich the digital twin.
If the purpose of the digital twin is, for example, to design a new building, then of course we need a much more detailed representation of the building geometry, building systems and surroundings. But this would be a different use case. Both cases would be examples of digital twins, but they would have a different purpose.
Even the connection to real time data from the original asset is an optional aspect in this context. If the building does not exist yet, no data would be available. Nevertheless, a digital twin based on physics-based simulations rooted firmly in building science still allows architects and engineers to make predictions, analyse options and make decisions with a high degree of confidence.
At IES, our ICL Digital Twin technology covers a number of use cases within the context of built environments varying in terms of their scale and purpose. This covers everything from:
All of the tools can be adapted and applied according to the specific use case and objectives of the user to unlock true value aligned to their specific objectives.
So, when selecting your Digital Twin, make sure you have a clear vision for what you want to achieve and remember that the most important aspect is the quality of data underpinning your digital twin and how your chosen solution can convert this data to information to provide the insights you need to realise your goals.